PP-38 Getting paediatric medicines on-label – scoping the needs for paediatric formulation of old medicines
Bibliographic record
Abstract
The Rosalind and Morris Goodman Family Paediatric For-mulations Centre of the CHU Sainte-Justine is a new non-profit organisation dedicated to supporting the development of safe and efficacious medicines that have a child-friendly formulation. One way of providing these paediatric medicines is to partner with industry to promote commercialising of a suitable paediatric formulation for commonly used medicines currently only avail-able in adult formulations. Working with all stakeholders including, pharmacists, paediatricians, Health Canada and the pharmaceutical industry, the Goodman Centre has identified commonly used off-label medicines that are currently compounded in pharmacies to produce pedi-atric formulations. In many cases, paediatric formulations and indications are available in other jurisdictions yet they have not been submitted by industry to Health Canada for regulatory approval. Using this novel approach, the Centre is to partnering with pharmaceutical companies to use existing data that has been submitted in other jurisdictions for Canadian approval. We will provide an overview of the Goodman Centre and outline the novel approach developed to improve access to paediatric for-mulations that we have undertaken.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".